Michael Emmerich
Universitair hoofddocent
- Naam
- Dr. M.T.M. Emmerich
- Telefoon
- +31 71 527 7094
- m.t.m.emmerich@liacs.leidenuniv.nl
Meer informatie over Michael Emmerich
Promovendi Universiteit Leiden
Andere promovendi
Foundations of multicriteria optimization and complex systems research:
- Set oriented optimization by numerical and natural computing methods
- Theory of indicators and preference models in multicriteria optimization
- Complex networks and parameterized computational complexity Applications of these algorithms in:
- Drug Discovery and Chemoinformatics
- Sustainable Engineering Design
- Scientific Data Management and Databases
I am involved in the Data Driven Drug Discovery Network (D4N) and in the Leiden Complex Network Network (LCN2).
For more information, see my personal website.
Universitair hoofddocent
- Wiskunde en Natuurwetenschappen
- Leiden Inst Advanced Computer Sciences
- Hao Wang, Stein B. van, Michael T. M.Emmerich & Bäck T.H.W. (2017), Time complexity reduction in efficient global optimization using cluster kriging. In: GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference.. 889-896.
- Boonstra S., Blom K. van der, Hofmeyer H. & Michael T. M.Emmerich (2017), Combined Super-Structured and Super-Structure Free Optimisation of Building Spatial Designs. In: Koch C., Tizani W., Ninic J. (red.) Intelligent computing in engineering.. 23-34.
- Blom K. van der, Boonstra S., Hofmeyer H., Bäck T. & Michael T. M.Emmerich (2017), Configuring Advanced Evolutionary Algorithms for Multicriteria Building Spatial Design Optimisation: IEEE.
- Michael T. M.Emmerich, Yang K., Deutz A., Hao Wang & Fonseca C.M. (2016), A Multicriteria Generalization of Bayesian Global Optimization. In: Pardalos M.P., Zhigljavsky A., Zilinskas J. (red.) Advances in Stochastic and Deterministic Global Optimization. Optimization and its Applications nr. 107: Springer. 229-243.
- Stein B. van, Hao Wang, Kowalczyk W.J., Michael T. M.Emmerich & Bäck T.H.W. (2016), Fuzzy clustering for Optimally Weighted Cluster Kriging. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).: IEEE. 978-1-5090-0627-4.
- Zhao J., Basto Fernandes V., Jiao L., Yevseyeva I., Asep Maulana A., Li R., Bäck T.H.W., Tang T. & Michael T. M.Emmerich (2016), Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms, Information Sciences 367-368: 80–104.
- Michael T. M.Emmerich, Deutz A.H., Li L., Asep Maulana A. & Yevseyeva I. (2016), Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making, Procedia Computer Science 100: 848–855.
- Yevseyeva I., Basto Fernandes V., Moorsel A. van, Janicke H. & Michael T. M.Emmerich (2016), Two-stage Security Controls Selection, Procedia Computer Science 100: 971-978.
- Hao Wang, Ren Y., Deutz A. & Michael T. M.Emmerich (2016), On Steering Dominated Points in Hypervolume Indicator Gradient Ascent for Bi-Objective Optimization. In: Schuetze O., Trujillo L., Legrand P., Maldonado Y. (red.) NEO 2015: Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico. Studies in Computational Intelligence nr. Studies in Computational Intelligence 663: Springer International Publishing. 175-203.
- Yang Z., Michael T. M.Emmerich, Bäck T.H.W. & Kok J.N. (2016), Multi-objective inventory routing with uncertain demand using population-based metaheuristics, Integrated Computer-Aided Engineering 23(3): 205-220.
- Yang Z., Osta J.P. van, Veen B. van, Krevelen R. van, Stam A., Kok J.N., Bäck T.H.W. & Michael T. M.Emmerich (2016), Dynamic vehicle routing with time windows in theory and practice, Natural computing 2016: 1-16.
- Stein B. van, Wang H., Kowalczyk W., Bäck T. & Emmerich M. (2015), Optimally Weighted Cluster Kriging for Big Data Regression. In: Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen (red.) Advances in Intelligent Data Analysis XIV. nr. 9385 Switzerland: Springer International Publishing. 310-321.
- Rosa Sequeira F., Frantz R.Z., Yevseyeva I., Michael T. M.Emmerich & Basto-Fernandes V. (2015), An EAI Based Integration Solution for Science and Research Outcomes Information Management, Procedia Computer Science 64: 894-901.
- Urselmann M., Engell S., Foussette C., Bäck T., Janus T., Tlatlik S., Gottschalk A & Emmerich M (2015), Designoptimierung thermischer Trennverfahren mit memetischen Algorithmen und Aspen Plus Prozessmodellen, Chemie Ingenieur Technik 87(8): 1060.
- Koch P., Wagner T., Emmerich M.T.M., Bäck T.H.W. & Konen W. (2015), Efficient multi-criteria optimization on noisy machine learning problems., Applied Soft Computing 29: 357-370.
- Li Rui, Michael T. M.Emmerich, Eggermont J., Bäck T.H.W., Schütz M., Dijkstra J. & Reiber J.H.C. (2012), Mixed Integer Evolution Strategies for Parameter Optimization, Evolutionary Computation Journal : 1-38.
- Michael T. M.Emmerich, Bäck T.H.W. & Li R. (2010), Improving Medical Image Analysis by Optimization, Eureka magazine : 8-11.
- Michael T. M.Emmerich, Li B.V.Y., Bender A., Faddiev E., Kruisselbrink J.W., Deutz A.H., Horst E. van der, IJzerman A.P. & Bäck T.H.W. (2008), Analysing Molecular Landscapes Using Random Walks and Information Theory. In: German Conference on Chemoinformatics. Goslar. 73.
- Michael T. M.Emmerich, Hopfe C., Marijt R., Hensen J. & Struck C. (2008), Evaluating Optimization Methodologies for Future Integration in Building Performance Tools. In: Ian Parmee (red.) ACDM 2008..
- Michael T. M.Emmerich, Li R., Eggermont J., Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2006), Optimizing Parameters of Coronary Vessel Image Analysis using Evolution Strategies. In: 18th Belgium-Netherlands Conference on Artificial Intelligence..
- Eggermont J., Bovenkamp E.G.P., Li R., Michael T. M.Emmerich, Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2006), Extending nk landscapes to the mixed-integer domain. In: 18th Belgium-Netherlands Conference on Artificial Intelligence..
- Michael T. M.Emmerich & Deutz A.H. (2006), A family of test problems with pareto-fronts of variable curvature based on super-spheres. In: MCDM 2006..
- Michael T. M.Emmerich, Shir O.M. & Bäck T.H.W. (2006), Multi-criteria optimization in laser pulse shaping using evolutionary algorithms and kriging metamodels. In: MCDM 2006..
- Jakumeit J., Michael T. M.Emmerich & Hediger F. (2006), Inverse modeling and numerical optimization of heater temperatures in a bridgman process. In: Modeling of Casting, Welding and Advanced Solidification Processes XI..
- Li Rui, Michael T. M.Emmerich, Bovenkamp E.G.P., Eggermont J., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2006), Mixed-Integer Evolution Strategies and Their Application to Intravascular Ultrasound Image Analysis. In: F. Rothlauf et al (red.) 8th European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP).: Springer Verlag. 415-426.
- Bäck T.H.W. & Michael T. M.Emmerich (2002), Evolution Strategies for Optimisation in Engineering Applications. In: Mang, H.A., Rammerstorfer, F.G., Eberhardsteiner, J. (red.) Proceedings Fifth World Congress on Computational Mechanics..
- Michael T. M.Emmerich, Beume N. & Naujoks B. (2005), An EMO algorithm using the hypervolume measure as selection criterion. In: Coello Coello C.A., Hernández Aguirre A., Zitzler E. (red.) Proceedings Evolutionary Multi-Criterion Optimization: Third Int'l Conference (EMO 2005). Berlin: Springer-verlag. 62-76.
- Li R., Michael T. M.Emmerich, Eggermont J., Bäck T.H.W., Schützand M., Dijkstra J. & Reiber J.H.C. (2013), Mixed Integer Evolution Strategies for Parameter Optimization, Evolutionary Computation Journal 21(1): 29-64.
- Wang P., Emmerich M.T.M., Li R., Tang K., Bäck T.H.W. & Yao X. (2015), Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance, IEEE Transactions on Evolutionary Computation 19(2): 188-200.
- Michael T. M.Emmerich, Deutz A.H. & Yevseyeva I. (2015), A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making, Procedia Computer Science 64(2015): 993-1000.
- Shir O.M., Michael T. M.Emmerich & Bäck T.H.W. (2010), Adaptive niche radii and niche shapes approaches for niching with the cma-es, Evolutionary Computation Journal 18(1): 97-126.
- Michael T. M.Emmerich, Deutz A.H. & Yevseyeva I. (2014), On Reference Point Free Weighted Hypervolume Indicators based on Desirability Functions and their Probabilistic Interpretation, Procedia Technology 16: 532-541.
- Li Rui, Etemaadi R., Michael T. M.Emmerich & Chaudron M.R.V. (2011), An evolutionary multiobjective optimization approach to component-based software architecture design. In: Proceedings IEEE Congress on Evolutionary Computation (CEC 2011).: IEEE. 432-439.
- Michael T. M.Emmerich, Deutz A.H. & Beume N. (2007), Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric, Hybrid Metaheuristics : 140-156.
- Li R., Eggermont J., Shir O.M., Michael T. M.Emmerich, Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2008), Mixed-Integer Evolution Strategies with Dynamic Niching. In: Günter Rudolph, Thomas Jansen, Simon M. Lucas, Carlo Poloni, Nicola Beume (red.) PPSN.: Springer. 246-255.
- Frantz R.Z., Sawicki S., Roos-Frantz F., Yevseyeva I. & Emmerich M.T.M. (2015), On using Markov Decision Processes to Model Integration Solutions for Disparate Resources in Software Ecosystems. In: Proceedings ICEIS 2015.. 260-267.
- Kruisselbrink J.W., Michael T. M.Emmerich & Bäck T.H.W. (2010), An Archive Maintenace Scheme for Finding Robust Solutions. In: Parallel Problem Solving from Nature (PPSN XI).: Springer. 214-223.
- Michael T. M.Emmerich & Deutz A.H. (2007), Test Problems based on Lame Superspheres. In: Evolutionary Multiobjective Optimization 2007 (EMO2007).: Springer. 922-936.
- Hupkens I., Deutz A.H., Yang K & Emmerich M.T.M. (2015), Faster Exact Algorithms for Computing Expected Hypervolume Improvement. In: Proceedings EMO.. 65-79.
- Basto-Fernandes V., Yevseyeva I., Méndez J.R., Zhao J., Fdez-Riverola F. & Michael T. M.Emmerich (2016), A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification, Applied Soft Computing 48: 111-123.
- Veen B.D.V., Michael T. M.Emmerich, Yang Z., Bäck T.H.W. & Kok J.N. (2013), Ant Colony Algorithms for the Dynamic Vehicle Routing Problem with Time Windows. In: Vicente JMF, Sánchez JRÁ, Paz López F, Toledo-Moreo FJ (red.) Natural and Artificial Computation in Engineering and Medical Applications - 5th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2013, Mallorca, Spain, June 10-14, 2013. Proceedings, Part II. nr. 7931: Springer. 1-10.
- Li R., Eggermont J., Michael T. M.Emmerich, Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2007), Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis. In: EvoWorkshops 2007.. 391-398.
- Kuijl A. v.d., Michael T. M.Emmerich & Li Rui (2008), A novel multi-objective optimization scheme for grid resource allocation. In: MGC 2008.. 7.
- Michael T. M.Emmerich & Fonseca C.M. (2011), Computing hypervolume contributions in low dimensions: asymptotically optimal algorithm and complexity results. In: EMO'11: Proceedings of the 6th international conference on Evolutionary multi-criterion optimization.: Springer. 121-135.
- Yang Z., Hao Wang, Yang K., Bäck T. & Michael T. M.Emmerich (2016), SMS-EMOA with multiple dynamic reference points. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD),.: IEEE Publishing. 282-288.
- Frantz R.Z., Sawicki S., Roos-Frantz F., Yevseyeva I. & Emmerich M. (2015), On Using Markov Decision Processes to Model Integration Solutions for Disparate Resources in Software Ecosystems. In: Hammoudi S., Maciaszek L., Teniente E. (red.) Proceedings of the 17th International Conference on Enterprise Information Systems. nr. 2 Barcelona: Scitepress. 260-267.
- Sella L., Michael T. M.Emmerich & Hille S.C. (2011), Simulations of Signaling Networks in B. Subtilis. In: Proceedings 8th European Conference on Mathematical and Theoretical Biology and Annual Meeting of the Society for Mathematical Biology..
- Yang Z., Li L., Deutz A.H., Bäck T. & Michael T. M.Emmerich (2016), SMS-EMOA with multiple dynamic reference points. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).: IEEE. 282-288.
- Wang P., Michael T. M.Emmerich, Li R., Tang K. & Bäck T.H.W. (2014), Convex Hull-Based Multi-objective Genetic Programming for Maximizing Receiver Operating Characteristic Performance, IEEE Transactions on Evolutionary Computation (99).
- Naujoks B., Beume N. & Michael T. M.Emmerich (2005), Metamodel-assisted SMS-EMOA applied to airfoil optimization tasks. In: Proceedings Evolutionary and Deterministic Methods for Design Optimisation and Control with Applications to Industrial and Societal Problems (EUROGEN'05)..
- Bürklen S., Deb K., Michael T. M.Emmerich, Kuefer K., Naujoks B., Ng A.H.C., Oyama A., Poles S., Ulrich T. & Witting K. (2012), What and how can we learn from Pareto fronts and sets? Working Group Pareto Sense. In: Proceedings Dagstuhl Seminar on Learning in Multiobjective Optimization.. 81-81.
- Kruisselbrink J.W., Aleman A., Michael T. M.Emmerich, IJzerman A.P., Bender A., Bäck T.H.W. & Horst E. van der (2009), Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching. In: Proceedings of the 11th Annual conference on Genetic and Evolutionary Computation (GECCO). New York: ACM. 217-224.
- Michael T. M.Emmerich & Nezhinsky A. (2015), Parameter Identification of Stochastic Gene Regulation Models by Indicator-based Evolutionary Level Set Approximation. In: Michael T. M.Emmerich, Tantar E., Legrand P., Tantar A., Alboaie L., Forascu C., Luchian H. (red.) EVOLVE 2015 - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation - International Conference (Book of Abstracts). IASI: A.I.C. University Iasi. 39-40.
- Etemaadi R., Michael T. M.Emmerich & Chaudron M.R.V. (2012), Problem-specific Search Operators for Metaheuristic Software Architecture Design. In: Proceedings of Search Based Software Engineering - 4th International Symposium, SSBSE 2012.: Springer. 267-272.
- Michael T. M.Emmerich & Nezshinsky A.E. (2014), Evolutionary Level Set Approximation Applied in Biological Systems Identification [extended abstract]. In: Preuss M. (red.) Proceedings PPSN 2014 - Workshop on Multimodal Optimization..
- Hao Wang, Schwab I. & Michael T. M.Emmerich (2015), Comparing Knowledge Representation Forms in Empirical Model Building. 170-178.
- Sanchez-Faddeev H., Michael T. M.Emmerich, Verbeek F.J., Henry A.H., Grimshaw S., Spaink H.P., Vlijmen H.W.T. van & Bender A. (2012), Using Multiobjective Optimization and Energy Minimization to Design an Isoform-Selective Ligand of the 14-3-3 Protein. In: Margaria T., Steffen B. (red.) Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies (ISoLA 2012).: Springer. 12-24.
- Li R., Michael T. M.Emmerich, Eggermont J., Dijkstra J & Reiber J.H.C. (2009), Radial Basis Function Network Models for metamodeling and Optimization in Mixed-Integer Spaces. Enschede: the University of twente. [overig]
- Wang P., Emmerich M., Li R., Tang K., Bäck T. & Yao X. (2015), Convex Hull-Based Multiobjective Genetic Programming for Maximizing Receiver Operating Characteristic Performance, IEEE Transactions on Evolutionary Computation 19(2): 188-200.
- Maulana A., Jiang Z., Liu J., Bäck T. & Emmerich M. (2015), Reducing Complexity in Many Objective Optimization Using Community Detection. In: Proceedings CEC 2015.. 3140-3147.
- Yang K., Deutz A.H., Yang Z., Bäck T.H.W. & Michael T. M.Emmerich (2016), Truncated expected hypervolume improvement: Exact computation and application. In: 2016 IEEE Congress on Evolutionary Computation (CEC).: IEEE Publishing. 4350-4357.
- Hernandez S., Adrian V., Schütze O. & Michael T. M.Emmerich (2014), Hypervolume Maximization via Set Based Netwon's Method. In: EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. nr. Advances in Intelligent Systems and Computing 288: Springer International Publishing. 15-28.
- Gheorge M., Snoeck M., Michael T. M.Emmerich, Bäck T.H.W., Goeman J. & Raz V. (2014), Major aging-associated RNA expressions change at two distinct age-positions, BMC Genomics 15(February): paper 132.
- Urselmann M., Michael T. M.Emmerich, Till J., Sand G. & Engell S. (2007), Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling, Engineering Optimization 39(5): 529-549.
- Hupkens I., Michael T. M.Emmerich & Deutz A.H. (2014), Faster Computation of Expected Hypervolume Improvement nr. TR 2014-03. Leiden, The Netherlands: LIACS, Leiden University.
- Yang Z., Emmerich M., Bäck T. & Kok J. (2015), Multicriteria Inventory Routing by Cooperative Swarms and Evolutionary Algorithms. In: Ferrández Vicente J.M., Álvarez-Sánchez J.R., Paz López F. de la, Toledo-Moreo F.C.O.J., Adeli H. (red.) Bioinspired Computation in Artificial Systems. nr. 9108 2015. 127-137.
- Kruisselbrink J.W., Michael T. M.Emmerich & Bäck T.H.W. (2011), An Archive Maintenance Scheme for Finding Robust Solutions. In: Parallel Problem Solving from Nature (PPSN XI).: Springer. 214-223.
- Blom K. van der, Boonstra S., Hofmeyer H. & Michael T. M.Emmerich (2016), A super-structure based optimisation approach for building spatial designs. In: Papadrakakis M., Papadopoulos V., Stefanou G., Plevris V. (red.) Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering.: National Technical University of Athens. 3409-3422.
- Blom K. van der, Boonstra S., Hofmeyer H. & Michael T. M.Emmerich (2016), Multicriteria building spatial design with mixed integer evolutionary algorithms. In: Handl J., Hart E., Lewis P.R., López-Ibáñez M., Ochoa G., Paechter B. (red.) Parallel Problem Solving from Nature – PPSN XIV. nr. LNCS 9921: Springer International Publishing AG. 453-462.
- Kruisselbrink J.W., Michael T. M.Emmerich & Bäck T.H.W. (2009), On the limitations of adaptive resampling in using the student's t-test evolution strategies. In: Proceedings of the 11th Annual conference on Genetic and Evolutionary Computation (GECCO). New York: ACM. 2649-2656.
- Klinkenberg J.W., Michael T. M.Emmerich & Deutz A.H. (2008), Expected improvement of the S-metric for finite Pareto front approximations. In: Conference on Multicriteria Decision Making 2008.: M. Ehrgott et al.
- Horst Eelke van der, Kruisselbrink J.W., Aleman A., Emmerich M.T.M., Bender Andreas & IJzerman A.P. (2010), Evolutionary Design of Selective Adenosine Receptor Ligands [poster], 2: 1-1.
- Li R., Michael T. M.Emmerich, Bäck T.H.W., Bovenkamp E.G.P., Eggermont J., Dijkstra J. & Reiber J.H.C. (2006), Optimizing Parameters of Intravascular Ultrasound Image Analysis Systems using Mixed-Integer Evolution Strategies. In: Adaptive Computing in Design and Manufacture (ACDM).: IP-CC Press. 283-284.
- Hao Wang, Michael T. M.Emmerich & Bäck T. (2016), Balancing Risk and Expected Gain in Kriging-based Global Optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC).: IEEE Publishing. 719-727.
- Guerreiro A.P., Fonseca C.M. & Michael T. M.Emmerich (2012), A Fast Dimension-Sweep Algorithm for the Hypervolume Indicator in Four Dimensions. In: Proceedings 24th Canadian Conference on Computational Geometry, CCCG 2012.. 77-82.
- Hao Wang, Deutz A.H., Bäck T.H.W. & Michael T. M.Emmerich (2017), Hypervolume Indicator Gradient Ascent Multi-objective Optimization. In: Trautmann H., Rudolph G., Klamroth K., Schütze O., Wiecek M., Jin Y., Grimme C. (red.) Evolutionary Multi-Criterion Optimization. nr. 10173 Cham: Springer International Publishing. 654-669.
- Michael T. M.Emmerich, Deutz H.A., Schütze O., Bäck T.H.W., Tantar E., Tantar A.-A., Del Moral P., Legrand P., Bouvry P. & Coello C.C. (red.) (2013), EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV Advances in Intelligent Systems and Computing: Springer-Verlag.
- Michael T. M.Emmerich, Deutz A.H. & Kruisselbrink J.W. (2012), The Cone-Based Hypervolume Indicator: Definition, Computation and Interpretation: LIACS, Universiteit Leiden.
- Tantar A.-A., Tantar E., Sun J.-Q., Zhang W., Ding Q., Schütze O., Michael T. M.Emmerich, Legrand P., Del Moral P. & Coello Coello C.A. (red.) (2014), EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V nr. Advances in Intelligent Systems and Computing 288: Springer International Publishing.
- Michael T. M.Emmerich, Li R., Zhang A., Flesch I. & Lucas P. (2008), Mixed-Integer Bayesian Optimization Utilizing A-Priori Knowledge on Parameter Dependences. In: 20th Belgian-Netherlands Conference on Artificial Intelligence (BNAIC). Enschede.
- Klinkenberg W., Michael T. M.Emmerich, Deutz A.H., Shir O.M. & Bäck T.H.W. (2008), A Reduced-Cost SMS-EMOA using Kriging, Self-Adaptation, and Parallelization. In: M. Ehrgott et al (red.) Conference on Multicriteria Decision Making 2008..
- Urselmann M., Foussette C., Janus T., Tlatlik S., Gottschalk A., Emmerich, M.T.M., Engell S. & Bäck T. (2016), Selection of a DFO Method for the Efficient Solution of Continuous Constrained Sub-Problems within a Memetic Algorithm for Chemical Process Synthesis. In: Friedrich T. (red.) Proceedings of the Genetic and Evolutionary Computation Conference 2016. New York: ACM. 1029-1036.
- Reehuis E.J.J., Olhofer M., Michael T. M.Emmerich, Sendhoff B. & Bäck T.H.W. (2013), Novelty and Interestingness Measures for Design-space Exploration. In: Proceedings 15th Annual Conference on Genetic and Evolutionary Computation - GECCO'13.: ACM Press. 1541-1548.
- Michael T. M.Emmerich, Hundemer J., Varcol M.C., Naujoks B. & Abdel-Maksoud M. (2006), Design optimization of ship propellers by means of advanced metamodel-assisted evolution strategies. In: International Conference on Design Optimization..
- Custodio A.L., Michael T. M.Emmerich & Madeira J.F.A. (2012), Recent Developments in Derivative-Free Multiobjective Optimisation, Computational Technology Reviews 5: 1-30.
- Kuijl A. v.d., Michael T. M.Emmerich & Li Rui (2010), A robust multi-objective resource allocation scheme incorporating uncertainty and service differentiation, Concurrency and Computation: Practice and Experience 22(3): 314-328.
- Rijn S. van, Emmerich M.T.M., Reehuis E. & Bäck T.H.W. (2015), Optimizing Highly Constrained Truck Loadings Using a Self-Adaptive Genetic Algorithm. In: Proceedings CEC 2015.. 227-234.
- Basto-Fernandes V., Yevseyeva I. & Michael T. M.Emmerich (2013), A Survey of Diversity-Oriented Optimization. In: Michael T. M.Emmerich, Deutz A.H. (red.) EVOLVE 2013 - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation: Extended Abstract Proceedings.: Leiden University. 101-109.
- Fonseca C.M., Yevseyeva I. & Michael T. M.Emmerich (2012), Risk and return in evolutionary multiobjective optimization. In: Dagstuhl Seminar on Learning in Multiobjective Optimization..
- Michael T. M.Emmerich, Deutz A.H. & Klinkenberg J.W. (2008), The computation of the expected improvement in dominated hypervolume of Pareto front approximations. Leiden: Universiteit Leiden.
- Till J., Sand G., Engell S., Michael T. M.Emmerich & Schönemann L. (2005), A hybrid algorithm for solving two-stage stochasticinteger problems by combining evolutionary algorithms and mathematical programming methods. In: Puigjaner L. (red.) Proceedings European Symposium Computer Aided Process Engineering (ESCAPE-15). Computer-aided Chemical Engineering Amsterdam: Elsevier. 187-192.
- Reehuis E., Kruisselbrink J.W., Deutz A.H., Bäck T.H.W. & Michael T. M.Emmerich (2011), Multiobjective optimization of water distribution networks using SMS-EMOA. In: Proceedings Evolutionary and Deterministic Methods for Design, Optimization and Control.: CIMNE. 269-279.
- Michael T. M.Emmerich, Urselmann M., Till J., Sand G. & Engell S. (2006), Hybrid ea/mip method for solving two-stage stochastic programming problems in chemical batch scheduling. In: 7th International Conference on Adaptive Computing in Design and Manufacture..
- Shir O.M., Michael T. M.Emmerich & Bäck T.H.W. (2007), Self-Adaptive Niching CMA-ES with Mahalanobis Metric. In: proceedings of IEEE Congress on Evolutionary Computation.: Research Publishing Services.
- Boonstra S., Blom K. van der, Hofmeyer H., Amor R. & Michael T. M.Emmerich (2016), Super-structure and super-structure free design search space representations for a building spatial design in multi-disciplinary building optimisation. In: Electronic proceedings of the 23rd International Workshop of the European Group for Intelligent Computing in Engineering.: Jagiellonian University ZPGK. 1-10.
- Hupkens I., Deutz A., Yang K. & Emmerich M. (2015), Faster Exact Algorithms for Computing Expected Hypervolume Improvement. In: Gaspar-Cunha A., Henggeler Antunes C., Coello C. (red.) Lecture Notes in Computer Science. nr. 9019 2015 Guimaraes: Springer Link. 65-79.
- Blom K. van der, Boonstra S., Hofmeyer H. & Michael T. M.Emmerich (2016), Multicriteria Building Spatial Design with Mixed Integer Evolutionary Algorithms. In: Handl J., Hart E., Lewis P.R., López-Ibáñez M., Ochoa G., Paechter B. (red.) Parallel Problem Solving from Nature – PPSN XIV 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings. nr. 9921 Cham: Springer International Publishers. 453-462.
- Wink S., Bäck T.H.W. & Michael T. M.Emmerich (2012), A Meta-Genetic Algorithm for Solving the Capacitated Vehicle Routing Problem. In: IEEE Congress on Evolutionary Computation 2012.. 1-8.
- Yang K., Michael T. M.Emmerich, Li R., Wang J. & Bäck T.H.W. (2014), Power Distribution Network Reconfiguration by Evolutionary Integer Programming. In: Proceedings Parallel Problem Solving from Nature (PPSN XIII). nr. LNCS 8672: Springer International Publishing. 11-23.
- Wagner T, Michael T. M.Emmerich, Deutz A.H. & Ponweiser W. (2011), On Expected-Improvement Criteria for Model-based Multi-objective Optimization. In: Parallel Problem Solving from Nature (PPSN XI).: Springer. 718-727.
- Asep Maulana A., Gametto V., Garlaschelli D., Yevesyeva I. & Michael T. M.Emmerich (2017), Modularities Maximization in Multiplex network Analysis Using Many-Objective Optimization nr. 2016 IEEE Symposium Series on Computational Intelligence (SSCI): IEEE. 1-8.
- Naujoks B., Beume N. & Michael T. M.Emmerich (2005), Multi-objective optimisation using S-metric selection: Application to three-dimensional solution spaces. In: McKay B., et al. (red.) Proceedings 2005 Congress on Evolutionary Computation (CEC'05). Piscataway NJ: IEEE Press. 1282-1289.
- Kerschke P., Wang H., Preuss M., Grimme C., Deutz A.H., Trautmann H. & Michael T. M.Emmerich (2016), Towards Analyzing Multimodality of Continuous Multiobjective Landscapes. In: Handl J., Hart E., Lewis P.R., López-Ibáñez M., Ochoa G., Paechter B. (red.) Parallel Problem Solving from Nature – PPSN XIV 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings. nr. 9921 Cham: Springer International Publishers. 962-972.
- Keizer M.J., Bergen M.J. van & Michael T. M.Emmerich (2013), An interactive web-based database application for collaborative collection, storage and retrieval of data on volcanic lakes. In: Proceedings International Conference IAVCEI 2013 - Scientific Assembly of the International Association of Volcanology and Chemistry of the Earth's Interior..
- Michael T. M.Emmerich, Deutz A.H. & Klinkenberg J.W. (2011), Hypervolume-based expected improvement: Monotonicity properties and exact computation. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2011).: IEEE. 2147-2154.
- Michael T. M.Emmerich, Deutz A.H. & Kruisselbrink J.W. (2011), On Quality Indicators for Finite Level-Set Representations. In: EVOLVE 2011 A bridge between Probability, Set Oriented Numerics and Evolutionary Computation..
- Doddareddy M.R., Westen G.J.P. van, Horst E. van der, Peironcely J., Corthals F., IJzerman A.P., Emmerich M.T.M., Jenkins J.L. & Bender A. (2009), Chemogenomics: Looking at biology through the lens of chemistry, Statistical Analysis and Data Mining 2(3): 149-160.
- Stein B. van, Wang H., Kowalczyk W., Michael T. M.Emmerich & Bäck T. (2016), Fuzzy clustering for Optimally Weighted Cluster Kriging. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).: IEEE. 939-945.
- Michael T. M.Emmerich & Deutz A.H. (2014), Time Complexity and Zeros of the Hypervolume Indicator Gradient Field. In: Proceedings EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. nr. Studies in Computational Intelligence 500: Springer International Publishling. 169-193.
- Yang K., Gaida D., Bäck T.H.W. & Emmerich M.T.M. (2015), Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant. In: Proceedings CEC 2015.. 1934-1942.
- Wang P., Michael T. M.Emmerich, Li R., Tang K., Xin Y. & Bäck T.H.W. (2013), Convex Hull-Based Multi-objective for Genetic Programming for Maximizing ROC Performance.
- Li Rui, Michael T. M.Emmerich, Eggermont J., Bäck T.H.W., Dijkstra J., Reiber H. & Schuetz M. (2011), Mixed Integer Evolution Strategies for Parameter Optimization, Evolutionary Computation Journal .
- Yang K., Li L., Deutz A.H., Bäck Th. & Michael T. M.Emmerich (2016), Preference-based multiobjective optimization using truncated expected hypervolume improvement. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).: IEEE. 276-281.
- Wang, H., Schwab I.: & Emmerich M. (2015), Comparing Knowledge Representation Forms in Empirical Model Building,. In: International Conference INTELLI2015. Malta: (INTELLI2015), ISBN: 978-1-61208-437-4, (2015), pp 170-178. 170-178.
- Kuijl A. v.d., Li Rui & Michael T. M.Emmerich (2008), A novel multi-objective optimization scheme for grid ressource allocation. In: 6th International Workshop on Middleware for Grid Computing - MGC 2008..
- Hao Wang, Bäck T.H.W. & Emmerich M.T.M. (2015), Multi-point efficient global optimization using Niching Evolution Strategy. In: Proceedings EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI.: Springer.
- Breukelaar R., Michael T. M.Emmerich & Bäck T.H.W. (2006), On interactive evolution strategies. In: Applications of Evolutionary Computing. Berlin / Heidelberg: Springer. 530-541.
- Urselmann M., Foussette C., Janus T., Tlatlik S., Gottschalk A., Emmerich M.T.M. & Bäck T.H.W. (2015), Derivative-Free Design Optimization of Chemical Processes by a Memetic Algorithm. In: Proceedings UK Workshop on Computational Intelligence..
- Hao Wang, Michael T. M.Emmerich & Bäck T.H.W. (2014), Mirrored orthogonal sampling with pairwise selection in evolution strategies. In: Proceedings 29th Annual ACM Symposium on Applied Computing.: ACM. 154-156.
- Zaeferer M., Bartz-Beielstein T., Naujoks B., Wagner T & Michael T. M.Emmerich (2012), Model-assisted Multi-criteria Tuning of an Event Detection Software under Limited Budgets, Schriftenreihe CIplus 10.
- Michael T. M.Emmerich, Giotis A., Özdemir M., Bäck T.H.W. & Giannakoglu K. (2002), Metamodel-Assisted Evolution Strategies. In: Merelo Guervos, J.J., Adamidis, P., Beyer, H.G., Fernandez-Villacanas, J.L., Schwefel, H.P. (red.) Proc. of the Seventh International Conference on Parallel Problem Solving from Nature. Berlin: Springer. 361-370.
- Kruisselbrink J.W., Michael T. M.Emmerich, Deutz A.H. & Bäck T.H.W. (2010), A Robust Optimization Approach using Kriging Metamodels for Robustness Approximation in the CMA-ES. In: IEEE Congress on Evolutionary Computation (CEC 2010).: IEEE. 1-8.
- Li R., Michael T. M.Emmerich, Eggermont J., Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2006), Mixed-Integer Optimization of Coronary Vessel Image Analysis using Evolution Strategies. In: In M. Keijzer et al (red.) 8th annual conference on Genetic and evolutionary computation.. 1645-1652.
- Leuven J., Emmerich M., Reehuis E. & Bäck T. (2015), User-derived mutation in highly constrained Truck Loading Optimization. In: Proceedings CEC 2015. Sendai, Japan: IEEE. 235-242.
- Michael T. M.Emmerich & Deutz A.H. (red.) (2013), EVOLVE 2013 - A Bridge Between Probability. Set Oriented Numerics, and Evolutionary Computation - Short Paper and Extended Abstract Proceedings: LIACS, Leiden University.
- Stein B. van, Michael T. M.Emmerich & Yang Z. (2013), Fitness Landscape Analysis of NK Landscapes and Vehicle Routing Problems by Expanded Barrier Trees. In: EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV.: Springer International Publishing. 75-89.
- Michael T. M.Emmerich, Deutz A.H., Kruisselbrink J.W. & Shukla P.K. (2013), Cone-Based Hypervolume Indicators: Construction, Properties, and Efficient Computation. In: Proceedings Seventh International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013). nr. Lecture Notes in Computer Science: Springer-Verlag. 111-127.
- Leuven J., Emmerich M.T.M., Reehuis E. & Bäck T.H.W. (2015), User-derived mutation in highly constrained Truck Loading Optimization. In: Proceedings CEC 2015.. 235-242.
- Michael T. M.Emmerich, Emilia T, Pierrick L, Alexandru A.T., Lenuta A., Corina F. & Henri L. (red.) (2015), EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation.
- Yevseyeva I., Basto-Fernandes V., Michael T. M.Emmerich & Moorsel A. van (2015), Selecting Optimal Subset of Security Controls, Procedia Computer Science 64: 1035-1042.
- Li Rui, Michael T. M.Emmerich, Eggermont J., Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2006), Mixed-Integer NK Landscapes. Parallel Problem Solving from Nature. In: PPSN.. 42-51.
- Yang K., Deutz A.H., Yang Z., Bäck T. & Michael T. M.Emmerich (2016), Truncated expected hypervolume improvement:Exact computation and application. In: 2016 IEEE Congress on Evolutionary Computation (CEC).: IEEE. 4350-4357.
- Michael T. M.Emmerich & Deutz A.H. (2010), Variance Monotonicity of the Expected Improvement of the 2D Hypervolume in Pareto Optimization. Leiden: Universiteit Leiden.
- Yang Z., Emmerich M., Bäck & T. (2015), Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities. In: Proceedings CEC 2015.. 2813-2819.
- Yang K., Gaida D., Bäck T. & Emmerich M. (2015), Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant. Sendai, Japan: IEEE. 1934-1942.
- Horst Eelke van der, Marques-Gallego P., Mulder-Krieger Thea, Veldhoven J.P.D. van, Kruisselbrink J.W., Aleman A., Michael T. M.Emmerich, Brussee J., Bender Andreas & IJzerman A.P. (2012), Multi-Objective Evolutionary Design of Adenosine Receptor Ligands, Journal of Chemical Information and Modeling 52(7): 1713-1721.
- Yang Z., Emmerich M.T.M., Bäck T.H.W. & Kok J.N. (2015), Multicriteria Inventory Routing by Cooperative Swarms and Evolutionary Algorithms. In: Proceedings IWINAC 2015.. 127-137.
- Shir O.M., Michael T. M.Emmerich, Bäck T.H.W. & Vrakking M.J.J. (2007), The Application of Evolutionary Multi-Criteria Optimization to Dynamic Molecular Alignment. In: proceedings of IEEE Congress on Evolutionary Computation.: Research Publishing Services.
- Yang K., Li L., Deutz A.H., Bäck T.H.W. & Michael T. M.Emmerich (2016), Preference-based multiobjective optimization using truncated expected hypervolume improvement. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD),.: IEEE Publishing.
- Michael T. M.Emmerich (2006), Sms-emoa: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research .
- Hupkens I. & Michael T. M.Emmerich (2013), Logarithmic-Time Updates in SMS-EMOA and Hypervolume-Based Archiving. In: EVOLVE - A bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV.: Springer International Publishing. 155-169.
- Michael T. M.Emmerich, Deutz A.H. & Kruisselbrink J.W. (2013), On Quality Indicators for Black-Box Level Set Approximation. In: Proceedings EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation.. 157-185.
- Chen J, Michael T. M.Emmerich, Li R., Kok J.N. & Bäck T.H.W. (2009), How to Do Recombination in Evolution Strategies: An Empirical Study. In: Third International Work conference on the Interplay between Natural and Artificial Computation.. 223-232.
- Koch, P., Wagner, T., Emmerich M. T. M., Bäck, T., Konen & W (2015), Efficient multi-criteria optimization on noisy machine learning problems, Applied Soft Computing 29: 357-370.
- Jakumeit J. & Michael T. M.Emmerich (2005), Inverse modelling and numerical optimization of heater temperatures in a Bridgman process. In: Proceedings of MCWASP Conference Modeling of Casting, Welding and Advanced Solidification Processes XI 2005. 2005.
- Hopfe C., Michael T. M.Emmerich, Marijt R. & Hensen J. (2012), Robust multi-criteria design optimization in building design. In: BSO12, Proceedings of the 1st IBPSA-England Conference Building Simulation and Optimization.: International Building Performance Simulation Association. 118-125.
- Cai F., Hao Wang, Tang X., Michael T. M.Emmerich & Verbeek F.J. (2016), Fuzzy Criteria in Multi-objective Feature Selection for Unsupervised Learning, Procedia Computer Science 102: 51-58.
- Li R., Michael T. M.Emmerich, Eggermont J., Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J & Reiber H. (2009), Optimizing a medical image analysis system using mixed-integer evolution strategies. In: S. Cagnoni et al (red.) Evolutionary Image Analysis and Signal Processing.: Springer.
- Li R., Michael T. M.Emmerich, Eggermont J., Bovenkamp E.G.P., Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2008), Metamodel-Assisted Mixed Integer Evolution Strategies and their Application to Intravascular Ultrasound Image Analysis. In: Proceedings of IEEE CEC. Hong Kong. 2764-2771.
- Zefferer M., Bartz-Beielsteen T., Naujoks B. & Wagner T. Emmerich M.T.M. (2013), Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets. In: Proceedings Evolutionary Multi-Criterion Optimization, EMO 2013. nr. LNCS 7811: Springer-Verlag. 756-770.
- Kruisselbrink J.W., Michael T. M.Emmerich, Deutz A.H. & Bäck T.H.W. (2011), Exploiting Overlap When Searching for Robust Optima. In: Parallel Problem Solving from Nature (PPSN XI).: Springer. 63-72.
- Michael T. M.Emmerich, Li B.V.Y., Bender Andreas, Faddiev E., Kruisselbrink J.W., Deutz A.H., Horst Eelke van der, IJzerman Adriaan P. & Bäck T.H.W. (2009), Analyzing Molecular Landscapes using Random walks and Information Theory [poster], Chemistry Central Journal 3(Suppl. 1).
- Yang Z., Emmerich M.T.M. & Bäck T.H.W. (2015), Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities. In: Proceedings CEC 2015.. 2813-2819.
- Michael T. M.Emmerich, Kruisselbrink J.W., Horst E. van der, IJzerman A.P., Bender A. & Bäck T.H.W. (2008), Combined Interactive and Automated Adaptive Search for Molecular Design. In: Proceedings of Adaptive Computing in Design and Manufacture 2008.: IPCC.
- Shukla P.K., Michael T. M.Emmerich & Deutz A.H. (2013), A Theoretical Analysis of Curvature Based Preference Models. In: Proceedings Seventh International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013). nr. Lecture Notes in Computer Science: Springer-Verlag. 367-382.
- Preuss M., Schönemann L. & Michael T. M.Emmerich (2005), Counteracting Genetic Drift and Disruptive Recombination in (mu + lambda)-EA on Multimodal Fitness Landscapes. In: Beyer H.-G., O'Reilly U.-M. (red.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005).: ACM. 865-872.
- Yevseyeva I., Guerreiro A.P., Michael T. M.Emmerich & Fonseca C.M. (2014), A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms. In: Parallel Problem Solving from Nature (PPSN XIII). nr. LNCS 8672: Springer. 672-681.
- Shir O.M., Preuss M., Naujoks B. & Michael T. M.Emmerich (2009), Enhancing decision space diversity in evolutionary multiobjective algorithms. In: Evolutionary Multi-Criterion Optimization, 5th International Conference, EMO 2009.: Springer. 95-109.
- Kruisselbrink J.W., Michael T. M.Emmerich, Bäck T.H.W., Bender A., IJzerman A.P. & Horst E. van der (2009), Combining aggegration with pareto optimization: A case study in evolutionary molecular design. In: Evolutionary Multi-Criterion Optimization, 5th International Conference, EMO 2009.: Springer. 453-467.
- Horst E. van der, Peironcely Julio E., IJzerman A.P., Beukers M.W., Lane J.R., Vlijmen H.W.T. van, Michael T. M.Emmerich, Okuno Yasushi & Bender A. (2010), A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization, BMC bioinformatics 11: 316.
- Klinkenberg J.W., Michael T. M.Emmerich, Deutz A.H., Shir O.M. & Bäck T.H.W. (2010), A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization. In: Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems.: Springer-Verlag.
- Basto-Fernandes, V., Yevseyeva I., Frantz R.Z., Grilo C., Pérez Díaz N. & Michael T. M.Emmerich (2014), An Automatic Generation of Textual Pattern Rules for Digital Content Filters Proposal, Using Grammatical Evolution Genetic Programming, Procedia Technology 16: 806-812.
- Kruisselbrink J.W., Reehuis E., Deutz A.H., Bäck T.H.W. & Michael T. M.Emmerich (2011), Using the uncertainty handling CMA-ES for finding robust optima. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation.: ACM. 877-884.
- Schütze O., Michael T. M.Emmerich, Xiong F.-R. & Sun J.-Q. (2014), Barrier Tree for Continuous Landscape by Means of Generalized Cell Mapping. In: EVOLVE - A Bridge between Probability, Set Oriented Optimization, and Evolutionary Computation (Short Paper Proceedings)..
- Eggermont J., Li R., Bovenkamp E.G.P., Marquering H.A., Michael T. M.Emmerich, Lugt A. van der, Bäck T.H.W., Dijkstra J. & Reiber J.H.C. (2008), Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning. In: Applications of Evolutionary Computing. Heidelberg: Springer Berlin / Heidelberg. 275-284.
- Li Rui, Etemaadi R., Michael T. M.Emmerich & Chaudron M.R.V. (2011), Automated Design of Software Architectures for Embedded Systems using Evolutionary Multiobjective Optimization. In: Proceedings Workshop on Applied Combinatorial Optimization..
- Doddareddy M.R., Westen G.J.P. van, Horst Eelke van der, Peironcely Julio E., Corthals Frans, IJzerman A.P., Michael T. M.Emmerich, Jenkins Jeremy L. & Bender Andreas (2010), Chemogenomics: Looking at biology through the lens of chemistry, Statistical Analysis and Data Mining 2(3): 149-160.
- Kerschke P., Hao Wang, Preuss M., Grimme C., Deutz A., Trautmann H. & Michael T. M.Emmerich (2016), Towards Analyzing Multimodality of Continuous Multiobjective Landscapes. In: Handl J., Hart E., Lewis P. R. , López-Ibáñez M., Ochoa G., Paechter B. (red.) Parallel Problem Solving from Nature – PPSN XIV.: Springer International Publishing. 962-972.
- Urselmann M., Engell S., Foussette C., Bäck T.H.W., Janus T., Tlatlik S., Gottschalk A. & Emmerich M.T.M. (2015), Designoptimierung thermischer Trennverfahren mit memetischen Algoritmen und AspenPlus Prozessmodellen, Chemie Ingenieur Technik 87(8): 1060.
- Zhang X., Wu J., Duan C., Emmerich M.T.M. & Bäck T.H.W. (2015), Towards robustness optimization of complex networks based on redundancy backup. In: Proceedings CEC 2015.. 2820-2826.
- Zhao J. Fernandes V.B., Licheng J., Yevseyeva I., Asep Maulana A., Li R., Bäck T.H.W. & Michael T. M.Emmerich (2014), Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm (. LIACS, Leiden University).
- Kruisselbrink J.W., Michael T. M.Emmerich & Bäck T.H.W. (2010), An Archive Based Method for Applying Evolutionary Algorithms to Find Robust Optima: Universiteit Leiden.
- Michael T. M.Emmerich, Deutz A.H., Kruisselbrink J.W. & Reehuis E. (2012), Evolutionary Approximation of Level Sets. In: EVOLVE 2012, A bridge between probability, set-oriented numerics, and evolutionary computation..
- Asep Maulana A., Jiang Z., Liu J., Bäck T.H.W. & Emmerich M.T.M. (2015), Reducing complexity in many objective optimization using community detection. In: Proceedings CEC 2015.. 3140-3147.
- Michael T. M.Emmerich, Deutz A.H., Li R. & Kruisselbrink J.W. (2010), Getting lost or getting trapped: on the effect of moves to incomparable points in multiobjective hillclimbing. In: GECCO (Companion).. 1963-1966.
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